Information foraging.

Pirolli, Peter; Card, Stuart K.

doi:10.1037/0033-295x.106.4.643

Cited by 1,198 publications

(660 citation statements)

References 36 publications

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“…Exploring a large ontology, particularly when it is unfamiliar to the user, can be characterized as information foraging [17]. Information foraging theory, drawing on ecological models of how animals hunt for food, proposes the notion of information scent.…”

Section: Resultsmentioning

confidence: 99%

A Novel Approach to Visualizing and Navigating Ontologies

Motta

Mulholland

Peroni

et al. 2011

Lecture Notes in Computer Science

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Abstract. Observational studies in the literature have highlighted low levels of user satisfaction in relation to the support for ontology visualization and exploration provided by current ontology engineering tools. These issues are particularly problematic for non-expert users, who rely on effective tool support to abstract from representational details and to be able to make sense of the contents and the structure of ontologies. To address these issues, we have developed a novel solution for visualizing and navigating ontologies, KC-Viz, which exploits an empirically-validated ontology summarization method, both to provide concise views of large ontologies, and also to support a 'middle-out' ontology navigation approach, starting from the most information-rich nodes (key concepts). In this paper we present the main features of KC-Viz and also discuss the encouraging results derived from a preliminary empirical evaluation, which suggest that the use of KC-Viz provides performance advantages to users tackling realistic browsing and visualization tasks. Supplementary data gathered through questionnaires also convey additional interesting findings, including evidence that prior experience in ontology engineering affects not just objective performance in ontology engineering tasks but also subjective views on the usability of ontology engineering tools.

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Section: Resultsmentioning

confidence: 99%

A Novel Approach to Visualizing and Navigating Ontologies

Motta

Mulholland

Peroni

et al. 2011

Lecture Notes in Computer Science

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“…The modeling and analysis of algorithms that guide optimization of search mode is relevant not only to the food quest of hunter-gatherers or, more generally, predators seeking prey (Zoroa et al, 2011), but also to practical matters of attempting to locate criminals (Brantingham, 2013), capture kidnappers (Zoroa et al, 2014), and encountering information in libraries (Sandstrom, 1994) or the internet (Pirolli and Card, 1999).…”

Section: The Broader Contextmentioning

confidence: 99%

Sit-and-wait versus active-search hunting: A behavioral ecological model of optimal search mode

Ross

Winterhalder

2015

Journal of Theoretical Biology

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We model the behavioral ecology of search mode for randomly moving predator and prey. Active-search is favored at low prey movement velocity, ambush at high prey velocity. Sit-and-wait mode is favored if predator movement is energetically costly. Ambush is favored if faster predator velocity alerts prey or impedes their detection. Optimal predator velocity in active-search mode balances costs against prey movement. a r t i c l e i n f o b s t r a c tDrawing on Skellam's (1958) work on sampling animal populations using transects, we derive a behavioral ecological model of the choice between sit-and-wait and active-search hunting. Using simple, biologically based assumptions about the characteristics of predator and prey, we show how an empirically definable parameter space favoring active-search hunting expands as: (1) the average rate of movement of prey decreases, or (2) the energetic costs of hunter locomotion decline. The same parameter space narrows as: (3) prey skittishness increases as a function of a hunter's velocity, or (4) prey become less detectable as a function of a hunter's velocity. Under either search tactic, encounter rate increases as a function of increasing prey velocity and increasing detection zone radius. Additionally, we investigate the roles of habitat heterogeneity and spatial auto-correlation or grouping of prey on the optimal search mode of a hunter, finding that habitat heterogeneity has the potential to complicate application of the model to some empirical examples, while the effects of prey grouping lead to relatively similar model outcomes. As predicted by the model, the introduction of the horse to the Great Plains and the introduction of the snowmobile to Arctic foraging communities decreased the metabolic costs of active-search and led to a change in normative hunting strategies that favored active-search in place of sit-and-wait hunting.

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“…Regardless of what information searchers use, when they choose specific documents to view, there must be "something" in the information they considered that gave them a cue that the document might be relevant. The goal of the automatic topic learning process is to capture this "information scent" [13].…”

Section: Automatic Topic Learningmentioning

confidence: 99%

“…Our goal in this research is to capture additional information about what users think is relevant to their active search goals, and subsequently use this to re-order the search results. This work is inspired by the traditional information retrieval approach to relevance feedback [15], as well as the concept of "information scent" [13].…”

Section: Introductionmentioning

confidence: 99%

Automatic Topic Learning for Personalized Re-Ordering of Web Search Results

Hoeber

Massie

2010

Advances in Soft Computing

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The fundamental idea behind personalization is to first learn something about the users of a system, and then use this information to support their future activities. When effective algorithms can be developed to learn user preferences, and when the methods for supporting future actions are achievable, personalization can be very effective. However, personalization is difficult in domains where tracking users, learning their preferences, and affecting their future actions is not obvious. In this paper, we introduce a novel method for providing personalized re-ordering of Web search results, based on allowing the searcher to maintain distinct search topics. Search results viewed during the search process are monitored, allowing the system to automatically learn about the users' current interests. The results of an evaluation study show improvements in the precision of the top 10 and 20 documents in the personalized search results after selecting as few as two relevant documents.

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Information foraging.

Cited by 1,198 publications

References 36 publications

A Novel Approach to Visualizing and Navigating Ontologies

A Novel Approach to Visualizing and Navigating Ontologies

Sit-and-wait versus active-search hunting: A behavioral ecological model of optimal search mode

Automatic Topic Learning for Personalized Re-Ordering of Web Search Results

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